A method, device, equipment and medium for accurate traffic allocation

Abstract

The present invention discloses a method, apparatus, device and medium for precise traffic allocation. The precise traffic allocation method includes: obtaining user behavior data and merchant operation data on a public domain traffic platform; determining consumer portrait data and merchant portrait data based on the user behavior data and the merchant operation data; determining a target merchant pool based on the merchant portrait data and a first business rule, and estimating the estimated traffic of the current merchant in the target merchant pool today according to the exposure volume in the target time window and a second business rule; adjusting the allocated traffic of the current merchant according to the estimated traffic of the current merchant today, the merchant traffic consumption data, the merchant traffic lower limit threshold and the merchant traffic upper limit threshold, and allocating the allocated traffic of the current merchant to the target user group based on the recall algorithm and the consumer portrait data. The technical solution of the embodiment of the present invention can perform reasonable and precise efficient allocation of traffic, and improve the satisfaction of both buyers and sellers.

Description

Technical Field

[0001] The present invention relates to the field of e-commerce technologies, and in particular, to a method, device, equipment, and medium for precise traffic allocation. Background Art

[0002] With the development of Internet technologies, new sales methods such as social media and e-commerce platforms have emerged like mushrooms after rain.

[0003] For current new sales methods, it is necessary to quickly respond to changes in user behavior through traffic allocation to achieve real-time traffic optimization. However, there may often be a contradiction between considering consumer satisfaction and ensuring the exposure value of goods. Moreover, for some niche or newly listed goods, due to limited sales transaction data, it is impossible to effectively determine an appropriate exposure volume for the goods, resulting in situations where traffic allocation often has low allocation efficiency and unreasonable allocation. Summary of the Invention

[0004] The present invention provides a method, device, equipment, and medium for precise traffic allocation to solve the problems of unreasonable traffic allocation and low allocation efficiency in current online commodity sales.

[0005] According to one aspect of the present invention, there is provided a method for precise traffic allocation, including:

[0006] Obtaining user behavior data and merchant operation data on a public domain traffic platform;

[0007] Based on the user behavior data and merchant operation data, determining consumer portrait data and merchant portrait data;

[0008] Based on the merchant portrait data and the first business rule, determining a target merchant pool, and estimating the current day's estimated traffic of the current merchant in the target merchant pool according to the target time window exposure volume and the second business rule;

[0009] Adjusting the allocated traffic of the current merchant according to the current day's estimated traffic of the current merchant, the merchant traffic consumption data, the merchant traffic lower limit threshold, and the merchant traffic upper limit threshold, and allocating the allocated traffic of the current merchant to the target user group based on the recall algorithm and the consumer portrait data.

[0010] According to another aspect of the present invention, there is provided a device for precise traffic allocation, including:

[0011] A data acquisition module, configured to obtain user behavior data and merchant operation data on a public domain traffic platform;

[0012] A user portrait module, configured to determine consumer portrait data and merchant portrait data based on the user behavior data and the merchant operation data;

[0013] A traffic prediction module, configured to determine a target merchant pool based on merchant portrait data and a first business rule, and predict the estimated traffic of the current merchant in the target merchant pool today according to the exposure volume in the target time window and a second business rule;

[0014] A traffic allocation module, configured to adjust the allocated traffic of the current merchant according to the estimated traffic of the current merchant today, the merchant traffic consumption data, the merchant traffic lower limit threshold, and the merchant traffic upper limit threshold, and allocate the allocated traffic of the current merchant to the target user group based on a recall algorithm and consumer portrait data.

[0015] According to another aspect of the present invention, there is provided an electronic device, the electronic device comprising:

[0016] At least one processor; and

[0017] A memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the traffic precise allocation method according to any embodiment of the present invention.

[0019] According to another aspect of the present invention, there is provided a computer-readable storage medium, the computer-readable storage medium stores computer instructions, and the computer instructions are used to implement the traffic precise allocation method according to any embodiment of the present invention when executed by a processor.

[0020] The technical solution of the embodiment of the present invention obtains user behavior data and merchant operation data on the public domain traffic platform, and then determines consumer portrait data and merchant portrait data based on the user behavior data and merchant operation data. Furthermore, based on the merchant portrait data and the first business rule, a target merchant pool is determined, and according to the exposure volume in the target time window and the second business rule, the estimated traffic of the current merchant in the target merchant pool today is predicted. Further, according to the estimated traffic of the current merchant today, the merchant traffic consumption data, the merchant traffic lower limit threshold, and the merchant traffic upper limit threshold, the allocated traffic of the current merchant is adjusted, and based on the recall algorithm and the consumer portrait data, the allocated traffic of the current merchant is distributed to the target user group. In this solution, the commodity resources in the public domain traffic platform are automatically and accurately allocated to the target user group most likely to generate conversion behavior, thereby improving the exposure effect and conversion rate of the commodity, helping the merchant increase the transaction volume, constructing an ecological structure that benefits the sales platform, users, and merchants, effectively avoiding the Matthew effect, solving the problems of unreasonable traffic allocation and low allocation efficiency in current online commodity sales, and being able to allocate traffic reasonably and accurately and efficiently, improving the satisfaction of both buyers and sellers.

[0021] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. Brief Description of the Drawings

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0023] Figure 1 It is a flowchart of a method for accurate traffic allocation provided in Embodiment 1 of the present invention;

[0024] Figure 2 It is a flowchart of a method for accurate traffic allocation provided in Embodiment 2 of the present invention;

[0025] Figure 3 It is a schematic structural diagram of an accurate traffic allocation device provided in Embodiment 3 of the present invention;

[0026] Figure 4 It shows a schematic structural diagram of an electronic device that can be used to implement the embodiments of the present invention. Detailed Description of the Embodiments

[0027] To enable those skilled in the art to better understand the solution of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0029] Embodiment 1

[0030] Figure 1 FIG. 10 is a flowchart of a method for accurately allocating traffic provided in Embodiment 1 of the present invention. This embodiment is applicable to the situation of reasonably and accurately allocating traffic for online sales merchants. This method can be executed by a traffic accurate allocation device, which can be implemented in the form of hardware and / or software, and the traffic accurate allocation device can be configured in an electronic device. As Figure 1 shown, the method includes:

[0031] Step 110, obtain user behavior data and merchant operation data on the public domain traffic platform.

[0032] Among them, the public domain traffic platform can be a platform that carries public domain traffic. Exemplarily, the public domain traffic platform can include but is not limited to social media and e-commerce platforms, etc. Public domain traffic refers to traffic that is publicly accessible on the Internet, that is, traffic obtained through channels such as search engines and social media.

[0033] Among them, user behavior data can be used to describe the online behavior of network users. Merchant operation data can be data that describes the merchant's business content and status. Exemplarily, user behavior data can include but is not limited to user clicks, browsing, and search records on the Internet, etc.

[0034] It should be noted that in the technical solution of the embodiment of the present invention, the acquisition, storage, and application of the user behavior data and the merchant operation data involved all comply with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0035] In the embodiment of the present invention, the user behavior data and the merchant operation data on the public domain traffic platform can be collected in real time based on the API (Application Programming Interface), or other data collection means (such as web crawlers, etc.).

[0036] Step 120: Determine the consumer portrait data and the merchant portrait data based on the user behavior data and the merchant operation data.

[0037] Among them, the consumer portrait data can be the attribute data describing the role of the consumer. The merchant portrait data can be the attribute data describing the role of the merchant. Exemplarily, the consumer portrait data can include but is not limited to the age, gender, hobbies of the consumer, and the geographical location corresponding to the user IP (Internet Protocol), etc. The merchant portrait data can include but is not limited to the commodity shipping address, sales categories, and sales pricing range, etc.

[0038] In the embodiment of the present invention, the user portrait tool can be used to analyze the user behavior data to obtain the consumer portrait data, and analyze the merchant operation data to obtain the merchant portrait data.

[0039] Optionally, the user behavior data and the merchant operation data can be preprocessed, such as data cleaning and filling, etc., to reduce data sparsity. The anonymization processing technology and data encryption technology can also be combined to ensure the security and privacy of the data. Then, based on the user portrait tool, the preprocessed data can be analyzed to obtain the consumer portrait data and the merchant portrait data.

[0040] Step 130: Determine the target merchant pool based on the merchant portrait data and the first business rule, and estimate the current day's estimated traffic of the current merchants in the target merchant pool according to the target time window exposure and the second business rule.

[0041] Among them, the first business rule can be a pre-set rule for dividing the merchant pool. The second business rule can be a rule for estimating the traffic required to be allocated to merchants. The target merchant pool can be the merchants selling the products of a certain product of interest. The estimated traffic today can be used to describe the size of the traffic estimated to be allocated to merchants. The exposure volume in the target time window can be the exposure volume of the product of interest in a preset time window (such as yesterday, which can be set according to user needs). The exposure volume refers to the number of times a product or information is seen by customers within a period of time. For example, in the merchant version of the food delivery app, the concept of exposure volume can be more precisely described as the number of exposed people, that is, the number of customers who see the store within a period of time.

[0042] In an embodiment of the present invention, the merchants corresponding to the merchant screen data can be divided by using the first business rule to obtain multiple merchant pools, and the target merchant pool can be determined from the merchant pools based on the recall strategy of the intelligent algorithm model. Furthermore, based on the second business rule and the proportion of the exposure volume in the target time window occupied by each merchant in the target merchant pool, the estimated traffic today of the current merchant in the target merchant pool can be estimated.

[0043] Optionally, the second business rule can be the following logic: According to the proportion of the exposure volume in the target time window occupied by each merchant in the target merchant pool, determine the traffic allocation weight of each merchant in the target merchant pool, and thus calculate the product of the exposure volume in the target time window and the traffic allocation weight of the current merchant in the target merchant pool to obtain the estimated traffic today of the current merchant in the target merchant pool.

[0044] Alternatively, the second business rule can also be the following logic: According to the exposure volume in the target time window and the moving average value algorithm, determine the traffic moving average value of each merchant in the target merchant pool, and thus determine the level interval in which the traffic moving average value corresponding to the current merchant falls, and use the product of the traffic allocation weight corresponding to the level interval and the exposure volume in the target time window as the estimated traffic today of the current merchant in the target merchant pool. Among them, the level interval and the corresponding relationship between the level interval and the traffic allocation weight can be agreed upon in the second business rule.

[0045] In a specific example, for the pre-processed consumer portrait data and merchant portrait data described above, model feature extraction is performed, so as to convert the extracted features into numerical vectors, and the finally converted numerical vectors are input into a machine learning model that has learned the first business rule and the second business rule, so as to estimate the estimated traffic today of the current merchant in the target merchant pool through the machine learning model.

[0046] Optionally, cross-validation and / or regularization techniques can be used to prevent the machine learning model from overfitting. The embodiment of the present invention adopts an interpretable machine learning model, which supports the explanation of the model decision-making process.

[0047] Step 140: Adjust the allocated traffic of the current merchant according to the estimated traffic for today of the current merchant, the merchant traffic consumption data, the merchant traffic lower limit threshold, and the merchant traffic upper limit threshold, and allocate the allocated traffic of the current merchant to the target user group based on the recall algorithm and the consumer portrait data.

[0048] Among them, the merchant traffic consumption data can be used to characterize the traffic consumption situation of the merchant, that is, it represents the total number of merchant exposure traffic. The merchant traffic lower limit threshold can be the minimum traffic allocated to the merchant set in advance. The merchant traffic upper limit threshold can be the maximum traffic allocated to the merchant set in advance. The target user group can be users interested in the concerned products, that is, users who are highly likely to perform behaviors such as paying and clicking on the concerned products.

[0049] In the embodiment of the present invention, the estimated traffic for today of the current merchant can be used as the all-day allocated traffic of the current merchant, and the merchant traffic consumption data is calculated in real time to monitor the merchant consumption traffic of the current merchant in real time. Thus, by integrating the multi-dimensional data of the merchant traffic consumption data, the merchant traffic lower limit threshold, and the merchant traffic upper limit threshold of the current merchant, the allocated traffic of the current merchant is adjusted according to a preset adjustment period. Furthermore, the target user group is obtained by analyzing the consumer portrait data and the current user real-time behavior data based on a deep learning model, and the adjusted allocated traffic corresponding to the current merchant is further allocated to the target user group.

[0050] Optionally, when the merchant traffic consumption data of the current merchant is greater than the merchant traffic upper limit threshold, it is timely determined whether there is a situation of malicious traffic diversion for the current merchant. If there is a situation of malicious traffic diversion, no traffic is allocated to this merchant anymore. If the merchant traffic consumption data is less than the merchant traffic lower limit threshold and the merchant traffic consumption data is much less than the estimated traffic for today, the allocated traffic of this merchant can be increased for the need of product exposure. For example, the increased traffic can be allocated to consumers who have never followed the current merchant. The embodiment of the present invention does not limit the specific type of consumers to be allocated.

[0051] The technical solution of the embodiment of the present invention obtains user behavior data and merchant operation data on a public domain traffic platform, thereby determining consumer portrait data and merchant portrait data based on the user behavior data and merchant operation data, and then determining a target merchant pool based on the merchant portrait data and a first business rule, and estimating today's estimated traffic of the current merchant in the target merchant pool according to the target time window exposure and a second business rule, and further adjusting the allocated traffic of the current merchant according to today's estimated traffic of the current merchant, merchant traffic consumption data, merchant traffic lower limit threshold and merchant traffic upper limit threshold, and allocating the allocated traffic of the current merchant to the target user group based on a recall algorithm and consumer portrait data. In this solution, the commodity resources in the public domain traffic platform are automatically and accurately allocated to the target user groups who are most likely to generate conversion behaviors, thereby improving the exposure effect and conversion rate of the commodities, helping merchants to increase their transaction volume, and building a win-win ecological structure for sales platforms, users and merchants. It effectively avoids the Matthew effect (that is, under the premise of considering the platform's rate of return, reasonable traffic allocation is made for head merchants and medium and long-tail merchants), solves the problems of unreasonable traffic distribution and low distribution efficiency in the current online commodity sales, and can reasonably, accurately and efficiently allocate traffic, thereby improving the satisfaction of both buyers and sellers.

[0052] Embodiment 2

[0053] Figure 2 This is a flow chart of a method for accurate traffic distribution provided in Example 2 of the present invention. This example is specific based on the above example and provides a specific optional implementation method for determining consumer portrait data and merchant portrait data based on user behavior data and merchant operation data. Figure 2 As shown, the method includes:

[0054] Step 210: Obtain user behavior data and merchant operation data on the public domain traffic platform.

[0055] Step 220: Based on the big data analysis tool, data mining is performed on the user behavior data and the merchant operation data to obtain user analysis data and merchant analysis data.

[0056] The user analysis data may be valid data extracted from the user behavior data, and the merchant analysis data may be valid data extracted from the merchant operation data.

[0057] In the embodiment of the present invention, big data analysis tools can be used to mine effective information in user behavior data and merchant operation data to obtain user analysis data and merchant analysis data.

[0058] Step 230: Determine consumer portrait data based on user analysis data, and determine merchant portrait data based on merchant analysis data.

[0059] In an embodiment of the present invention, user analysis data can be input into a user portrait tool to construct consumer portrait data through the user portrait tool. Similarly, merchant operation data can be input into the user portrait tool to construct merchant portrait data through the user portrait tool.

[0060] Step 240: Determine a target merchant pool based on the merchant portrait data and the first business rule, and estimate the current day's estimated traffic of the current merchant in the target merchant pool according to the exposure volume in the target time window and the second business rule.

[0061] In an optional embodiment of the present invention, determining the target merchant pool based on the merchant portrait data and the first business rule may include: performing data clustering on the merchant portrait data according to the first business rule to obtain a clustered merchant pool; determining the target merchant pool according to the clustered merchant pool and the target commodity type.

[0062] Among them, the clustered merchant pool may be a merchant pool obtained by clustering and dividing the merchants corresponding to the merchant portrait data. The target commodity type may be the commodity type of the concerned commodity.

[0063] In an embodiment of the present invention, data clustering can be performed on the merchant portrait data based on the first business rule to divide the merchants corresponding to the merchant portrait data under the same cluster into the same merchant pool, that is, divide the merchants selling the same category of commodities into one merchant pool to obtain a clustered merchant pool.

[0064] In an optional embodiment of the present invention, estimating the current day's estimated traffic of the current merchant in the target merchant pool according to the exposure volume in the target time window and the second business rule may include: determining the traffic allocation weight of the merchants in the target merchant pool based on the second business rule; estimating the current day's estimated traffic of the current merchant in the target merchant pool according to the exposure volume in the target time window and the traffic allocation weight of the merchants in the target merchant pool.

[0065] In an embodiment of the present invention, the proportion of the exposure volume in the target time window occupied by each merchant in the target merchant pool can be scored based on the second business rule, so as to determine the traffic allocation weight of each merchant in the target merchant pool based on the corresponding relationship between the score and the weight specified in the second business rule. Further, the corresponding traffic allocation weight of the current merchant is determined from the traffic allocation weights of the merchants in the target merchant pool, so as to calculate the product of the exposure volume in the target time window and the corresponding traffic allocation weight of the current merchant to obtain the current day's estimated traffic of the current merchant in the target merchant pool.

[0066] Step 250: Adjust the allocated traffic of the current merchant according to the estimated traffic for today of the current merchant, the merchant traffic consumption data, the merchant traffic lower limit threshold, and the merchant traffic upper limit threshold, and allocate the allocated traffic of the current merchant to the target user group based on the recall algorithm and the consumer portrait data.

[0067] In an alternative embodiment of the present invention, adjusting the allocated traffic of the current merchant according to the estimated traffic for today of the current merchant, the merchant traffic consumption data, the merchant traffic lower limit threshold, and the merchant traffic upper limit threshold may include: obtaining the target time period for traffic pre-allocation; and adjusting the allocated traffic of the current merchant according to the estimated traffic for today of the current merchant, the merchant traffic consumption data, the merchant traffic lower limit threshold, and the merchant traffic upper limit threshold according to the target time period for traffic pre-allocation.

[0068] Among them, the target time period for traffic pre-allocation may be the time period for allocating the initial traffic to the current merchant. Exemplarily, the target time period for traffic pre-allocation may be configured as three time periods: morning, noon, and evening, such as 8 am, 12 noon, and 6 pm. The specific time points of the target time period for traffic pre-allocation are not limited in the embodiments of the present invention.

[0069] In the embodiments of the present invention, the target time period for traffic pre-allocation configured by the sales platform may be obtained, so that the estimated traffic for today is used as the all-day traffic and allocated during the target time period for traffic pre-allocation to initialize the initial traffic of the current merchant under the target time period for traffic pre-allocation, and further adjust the allocated traffic of the current merchant according to the merchant traffic consumption data, the merchant traffic lower limit threshold, and the merchant traffic upper limit threshold according to the preset adjustment period.

[0070] In an alternative embodiment of the present invention, after allocating the allocated traffic of the current merchant to the target user group, it may further include: obtaining the real-time exposure volume of the current merchant and the response behavior data of the traffic-recommended users; and adjusting the allocated traffic according to the real-time exposure volume of the current merchant and the response behavior data of the traffic-recommended users according to the traffic optimization strategy.

[0071] Among them, the traffic-recommended users may be the recommended objects corresponding to the traffic of the current merchant. The response behavior data may be used to characterize the response behavior of consumers to the recommended traffic of the merchant. The traffic optimization strategy may be a pre-configured strategy for adjusting traffic allocation.

[0072] In the embodiments of the present invention, the real-time exposure volume of the current merchant and the response behavior data of the traffic-recommended users can be obtained in real time. Thus, according to the real-time exposure volume of the current merchant, the number of times the current merchant is browsed by customers can be determined. Then, according to the response behavior data of the traffic-recommended users, the number of consumers who actually make payments can be determined. Furthermore, the ratio of the aforementioned number of browsing times to the number of consumers is used as the exposure conversion rate. When the exposure conversion rate does not meet the lower threshold of the exposure conversion rate, the allocated traffic is adjusted based on the traffic optimization strategy.

[0073] Optionally, the traffic allocation strategy can be continuously and compliantly optimized based on the merchant's satisfaction with the exposure conversion rate.

[0074] In an alternative embodiment of the present invention, before adjusting the allocated traffic according to the traffic optimization strategy, it may further include: determining a test control merchant that matches the current merchant; conducting a comparative experiment on the current merchant and the test control merchant based on the traffic optimization test rules to obtain the traffic optimization strategy.

[0075] Among them, the test control merchant can be a merchant benchmarked against the current merchant, that is, a merchant with the same sales category as the current merchant and a sales volume within a certain preset error range. The traffic optimization test rules can be any known experimental rules corresponding to a control experiment. The traffic optimization test rules can include, but are not limited to, the A / B test experimental rules.

[0076] In the embodiments of the present invention, a test control merchant that matches the current merchant can be determined in the target merchant pool. Thus, a comparative experiment is conducted on the current merchant and the test control merchant based on the traffic optimization test rules, and the traffic allocation strategy applied by the merchant with a higher exposure conversion rate is used as the traffic optimization strategy.

[0077] In the technical solution of the embodiment of the present invention, by obtaining user behavior data and merchant operation data on the public domain traffic platform, based on the user behavior data and merchant operation data, consumer portrait data and merchant portrait data are determined. Then, based on the merchant portrait data and the first business rule, a target merchant pool is determined, and according to the exposure volume in the target time window and the second business rule, the estimated traffic of the current merchant in the target merchant pool for today is predicted. Further, according to the estimated traffic of the current merchant for today, the merchant traffic consumption data, the merchant traffic lower limit threshold, and the merchant traffic upper limit threshold, the allocated traffic of the current merchant is adjusted, and based on the recall algorithm and the consumer portrait data, the allocated traffic of the current merchant is allocated to the target user group. In this solution, the commodity resources in the public domain traffic platform are automatically and accurately allocated to the target user group most likely to generate conversion behavior, thereby improving the exposure effect and conversion rate of the commodity, helping the merchant increase the transaction volume, constructing an ecological structure in which the sales platform, users, and merchants win-win, solving the problems of unreasonable traffic allocation and low allocation efficiency in current online commodity sales, and being able to allocate traffic reasonably and accurately and efficiently, improving the satisfaction of both buyers and sellers.

[0078] Embodiment III

[0079] Figure 3 FIG. is a schematic structural diagram of a traffic precise allocation device provided in Embodiment III of the present invention.

[0080] As Figure 3 shown, the device includes:

[0081] A data acquisition module 310, configured to acquire user behavior data and merchant operation data on the public domain traffic platform;

[0082] A user portrait module 320, configured to determine consumer portrait data and merchant portrait data based on the user behavior data and the merchant operation data;

[0083] A traffic estimation module 330, configured to determine a target merchant pool based on the merchant portrait data and the first business rule, and predict the estimated traffic of the current merchant in the target merchant pool for today according to the exposure volume in the target time window and the second business rule;

[0084] A traffic allocation module 340, configured to adjust the allocated traffic of the current merchant according to the estimated traffic of the current merchant for today, the merchant traffic consumption data, the merchant traffic lower limit threshold, and the merchant traffic upper limit threshold, and allocate the allocated traffic of the current merchant to the target user group based on the recall algorithm and the consumer portrait data.

[0085] The technical solution of the embodiment of the present invention obtains user behavior data and merchant operation data on the public domain traffic platform, and then determines consumer portrait data and merchant portrait data based on the user behavior data and merchant operation data. Furthermore, based on the merchant portrait data and the first business rule, a target merchant pool is determined, and according to the exposure volume in the target time window and the second business rule, the estimated traffic of the current merchant in the target merchant pool today is predicted. Further, according to the estimated traffic of the current merchant today, the merchant traffic consumption data, the merchant traffic lower limit threshold, and the merchant traffic upper limit threshold, the allocated traffic of the current merchant is adjusted, and based on the recall algorithm and the consumer portrait data, the allocated traffic of the current merchant is allocated to the target user group. In this solution, the commodity resources in the public domain traffic platform are automatically and accurately allocated to the target user group most likely to generate conversion behavior, thereby improving the exposure effect and conversion rate of the commodity, helping the merchant increase the transaction volume, constructing an ecological structure in which the sales platform, users, and merchants win-win, solving the problems of unreasonable traffic allocation and low allocation efficiency in current online commodity sales, and being able to allocate traffic reasonably and accurately and efficiently, improving the satisfaction of both buyers and sellers.

[0086] Optionally, the user portrait module 320 is used to perform data mining on the user behavior data and the merchant operation data based on a big data analysis tool to obtain user analysis data and merchant analysis data; determine the consumer portrait data according to the user analysis data, and determine the merchant portrait data according to the merchant analysis data.

[0087] Optionally, the traffic estimation module 330 includes a target merchant pool determination unit and a traffic estimation unit. The target merchant pool determination unit is used to perform data clustering on the merchant portrait data according to the first business rule to obtain a clustered merchant pool; determine the target merchant pool according to the clustered merchant pool and the target commodity type.

[0088] Optionally, the traffic estimation unit is used to determine the traffic allocation weight of the merchants in the target merchant pool based on the second business rule; predict the estimated traffic of the current merchant in the target merchant pool today according to the exposure volume in the target time window and the traffic allocation weight of the merchants in the target merchant pool.

[0089] Optionally, the traffic allocation module 340 is used to obtain the target time period for traffic pre-allocation; determine the allocated traffic of the current merchant according to the estimated traffic of the current merchant today, the merchant traffic consumption data, the merchant traffic lower limit threshold, and the merchant traffic upper limit threshold according to the target time period for traffic pre-allocation.

[0090] Optionally, the traffic precise allocation device further includes an allocation traffic adjustment module, configured to obtain the real-time exposure volume of the current merchant and the response behavior data of the traffic recommended users; and adjust the allocated traffic according to the real-time exposure volume of the current merchant and the response behavior data of the traffic recommended users according to a traffic optimization strategy.

[0091] Optionally, the traffic precise allocation device further includes a traffic optimization strategy determination module, configured to determine a test control merchant that matches the current merchant; and conduct a comparative experiment on the current merchant and the test control merchant based on traffic optimization test rules to obtain the traffic optimization strategy.

[0092] The traffic precise allocation device provided by the embodiments of the present invention can execute the traffic precise allocation method provided by any embodiment of the present invention, and has corresponding function modules and beneficial effects for executing the method.

[0093] Embodiment 4

[0094] Figure 4 The structural schematic diagram of an electronic device that can be used to implement the embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, a personal digital assistant, a cellular phone, a smart phone, a wearable device (such as a helmet, glasses, a watch, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0095] As Figure 4 shown, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc. Among them, the memory stores a computer program executable by at least one processor, and the processor 11 can execute various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0096] Multiple components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disc, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0097] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the traffic precise allocation method.

[0098] In some embodiments, the traffic precise allocation method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the traffic precise allocation method described above can be executed. Alternatively, in other embodiments, the processor 11 can be configured to execute the traffic precise allocation method in any other suitable way (e.g., by means of firmware).

[0099] The various embodiments of the systems and technologies described above in this article can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs, which can be executed and / or interpreted on a programmable system including at least one programmable processor, the programmable processor can be a special or general programmable processor, and can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0100] A computer program for implementing the method of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The computer programs can be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0101] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0102] In order to provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which the user can provide input to the electronic device. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, speech input, or tactile input).

[0103] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected with each other by digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.

[0104] A computing system can include a client and a server. The client and the server are generally far from each other and typically interact through a communication network. The client-server relationship is created by computer programs that run on the respective computers and have a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, and solves the defects of traditional physical hosts and VPS servers, such as high management difficulty and weak business scalability.

[0105] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and this is not limited herein.

[0106] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for precise flow distribution, characterized in that, Including: Obtaining user behavior data and merchant operation data on the public domain traffic platform; Based on the user behavior data and the merchant operation data, determining consumer portrait data and merchant portrait data; According to the first business rule, performing data clustering on the merchant portrait data to obtain a clustering merchant pool; Determining a target merchant pool according to the clustering merchant pool and the target commodity type; Based on the second business rule, determining the traffic allocation weight of the merchants in the target merchant pool; Estimating the estimated traffic of the current merchant in the target merchant pool today according to the exposure volume in the target time window and the traffic allocation weight of the merchants in the target merchant pool; According to the estimated traffic of the current merchant today, the merchant traffic consumption data, the merchant traffic lower limit threshold, and the merchant traffic upper limit threshold, adjusting the allocated traffic of the current merchant, and based on the recall algorithm and the consumer portrait data, allocating the allocated traffic of the current merchant to the target user group.

2. The method according to claim 1, wherein The determining of the consumer portrait data and the merchant portrait data based on the user behavior data and the merchant operation data includes: Based on a big data analysis tool, performing data mining on the user behavior data and the merchant operation data to obtain user analysis data and merchant analysis data; According to the user analysis data, determining the consumer portrait data, and according to the merchant analysis data, determining the merchant portrait data.

3. The method according to claim 1, characterized in that The adjusting of the allocated traffic of the current merchant according to the estimated traffic of the current merchant today, the merchant traffic consumption data, the merchant traffic lower limit threshold, and the merchant traffic upper limit threshold includes: Obtaining the target time period for traffic pre-allocation; According to the estimated traffic of the current merchant today, the merchant traffic consumption data, the merchant traffic lower limit threshold, and the merchant traffic upper limit threshold, adjusting the allocated traffic of the current merchant according to the target time period for traffic pre-allocation.

4. The method according to claim 1, wherein After the allocating of the allocated traffic of the current merchant to the target user group, it further includes: Obtaining the real-time exposure volume of the current merchant and the response behavior data of the traffic-recommended users; According to the real-time exposure volume of the current merchant and the response behavior data of the traffic-recommended users, adjusting the allocated traffic according to the traffic optimization strategy.

5. The method according to claim 4, characterized in that, Before the adjusting of the allocated traffic according to the traffic optimization strategy, it further includes: Determining a test control merchant matching the current merchant; Based on the traffic optimization test rule, conducting a comparative experiment on the current merchant and the test control merchant to obtain the traffic optimization strategy.

6. A flow rate precise distribution device, characterized in that, Including: A data acquisition module for obtaining user behavior data and merchant operation data on the public domain traffic platform; A user portrait module for determining consumer portrait data and merchant portrait data based on the user behavior data and the merchant operation data; The target merchant pool determination unit of the traffic estimation module for performing data clustering on the merchant portrait data according to the first business rule to obtain a clustering merchant pool; Determining a target merchant pool according to the clustering merchant pool and the target commodity type; The traffic estimation unit of the traffic estimation module is used to determine the traffic allocation weight of the merchants in the target merchant pool based on the second business rule; Estimate the estimated traffic of the current merchant in the target merchant pool today according to the exposure volume in the target time window and the traffic allocation weight of the merchants in the target merchant pool; The traffic allocation module is used to adjust the allocated traffic of the current merchant according to the estimated traffic of the current merchant today, the merchant traffic consumption data, the merchant traffic lower limit threshold, and the merchant traffic upper limit threshold, and allocate the allocated traffic of the current merchant to the target user group based on the recall algorithm and the consumer portrait data.

7. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the traffic precise allocation method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, and the computer instructions are used to implement the traffic precise allocation method according to any one of claims 1-5 when executed by a processor.

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